Agriculture Reference
In-Depth Information
households within the district have income levels that range between R0 and R6, 000. The
District Municipality has the second highest population of all the districts with more than 1,
504, 411 inhabitants [29]. For a mostly rural district it also has a high population density of 90
people per square kilometre. The Amatole District Municipality is named after the legendary
Amatole Mountains and is the most diverse District Municipality in the Province. Two-thirds
of the District is made up of ex-homeland areas. The District has a moderate Human Devel‐
opment Index of 0. 52 with over 1, 635, 433 inhabitants [30], and a moderately high population
density of 78 people per square kilometre. The population is mainly African with some whites
and coloureds. Amatole District Municipality has the second highest economy in the province.
The Eastern Cape Province is bordering KwaZulu-Natal with similarities in the socio-economic
status and rurality of the two Provinces. Both Provinces' economic dependence is on agricul‐
ture with huge potential for organic agriculture development. The Eastern Cape is also a major
consumer of produce from KwaZulu-Natal. A total of 400 respondents were interviewed,
representing 200 farmer respondents from KwaZulu-Natal and 200 consumer respondents
from Eastern Cape Provinces. The survey farmers in Umbumbulu District, KwaZulu-Natal
were stratified into three groups: fully-certified organic farmers, partially-certified organic
farmers and non-organic farmers. While the 48 fully-certified farmers and 103 partially-
certified farmers were purposively selected, the sample of 49 non-organic farmers was
randomly selected within the same region from a sample frame constructed from each of the
five neighbouring wards. The survey was conducted by a team of trained enumerators from
the study area. These enumerators had to be fluent in both English and Zulu. A questionnaire
was used to record all household activities (farm and non-farm), enterprise types, crop areas
and production levels, inputs, expenditures and sales for the past season. The questionnaires
also captured socio-economic and institution data such as household characteristics, land size
and tenure arrangements, farm characteristics and investment in assets. Other questions
related to farmers' management capacity and demographic characteristics such as the supply
of on-farm family labour and education status.
The farmers' risk attitude was elicited using the experimental gambling approach as outlined
by [31]. Here, the study farmers were presented with a series of choices among sets of
alternative prospects (gambles) that do not involve real money payments. Respondents were
required to make a simple choice among eight gambles whose outcomes were determined by
a flip of a coin. The experimental approach remedies some of the more serious measurement
flaws of the direct elicitation utility (DEU) interview method reporting that evidence on risk
aversion using direct elicitation utility through pure interviews is unreliable, nonreplicable
and misleading even if one is interested only in a distribution of risk aversion rather than
reliable individual measurements [31, 32]. The farmers were further asked in the field survey
to give their perceptions of the main sources of risk that affect their farming activity by ranking
a set of 20 potential sources of risk on like rt-type scales ranging from 1 (no problem) to 3
(severe problem). These sources of risk were developed from findings of the research survey
and from past research on the sources of risk in agriculture, challenges that smallholder farmers
face in trying to access formal supply chains. The farmers were also requested to score any
other sources of risk(s) that they wanted to add to the list of hypothesized sources of risk. These
